Information theory, multivariate dependence, and genetic network inference

2004/06/07 by Ilya Nemenman, Nemenman, Ilya · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Gene Regulatory Network Analysis #cs.IT #math.IT #math.ST #physics.data-an #q-bio.GN #q-bio.QM #stat.TH

paper · pdf · doi:10.48550/arxiv.q-bio/0406015

8 pages, 2 figures

arxiv created 2004/06/07 · arxiv updated 2009/12/01

Abstract

We define the concept of dependence among multiple variables using maximum entropy techniques and introduce a graphical notation to denote the dependencies. Direct inference of information theoretic quantities from data uncovers dependencies even in undersampled regimes when the joint probability distribution cannot be reliably estimated. The method is tested on synthetic data. We anticipate it to be useful for inference of genetic circuits and other biological signaling networks.

Cited by

• Conditional Network Analysis Identifies Candidate Regulator Genes in Human B Cells
• Info-Clustering: A Mathematical Theory for Data Clustering
• On The Reconstruction of Interaction Networks with Applications to Transcriptional Regulation

Related

• Measuring Dependence with Matrix-based Entropy Functional
• From statistical mechanics to information theory: understanding biophysical information-processing systems
• A primer on information theory, with applications to neuroscience
• Estimating mutual information and multi--information in large networks
• Information Theory and Statistics: an overview